I would like to chime in here on something that you mentioned earlier. You wanted to use the profiler on "actual data." To be clear, that is not really possible. Think of this simple example:

X Y

10 90

10 94

20 70

20 74

At what value of Y should the profiler be at when X=10? The logical choice is 92, but that is the expected mean for the response based on a model. Similarly, what would should the Y value be when X=15? No way to get that without a model.

So the proper abbreviated process, at a very high level, is to:

1) Collect your data (there should be many steps ahead of this to ensure the proper data is collected in the proper fashion, etc.).

2) Fit a model to your data (the model will be based on step #1 and all of your assumptions and issues discussed prior to step 1),

3) Assess the fit of your model. If the model does not fit well, you should not be using the profiler because it will not be accurate.

4) If the model does fit well, use the profiler to generate some predictions.

5) **** VERY IMPORTANT STEP ***** VERIFY that the model is predicting the future data. Your model may fit the data used to build the model -- it should based on step #3. But will it predict future observations? Don't know until you verify it. There are several possible approaches to doing this depending on situation and circumstance, but those would be topics covered in many books on predictive modeling.

Dan Obermiller